Jan-Philipp Kolb
23 November 2017
maptools, sp, tmap)library(maps)
map()maps - etwas detailierterGrenzen sind recht grob:
map("world", "Germany")maps - Mehr Informationdata(world.cities)
map("france")
map.cities(world.cities,col="blue")maptoolsmaptools hat intuitivere Bedienung, zudem können Shapefiles verarbeitet werden.library(maptools)
data(wrld_simpl)
plot(wrld_simpl,col="royalblue").shp)?head(wrld_simpl@data)| FIPS | ISO2 | ISO3 | UN | NAME | AREA | POP2005 | REGION | SUBREGION | LON | LAT | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ATG | AC | AG | ATG | 28 | Antigua and Barbuda | 44 | 83039 | 19 | 29 | -61.783 | 17.078 |
| DZA | AG | DZ | DZA | 12 | Algeria | 238174 | 32854159 | 2 | 15 | 2.632 | 28.163 |
| AZE | AJ | AZ | AZE | 31 | Azerbaijan | 8260 | 8352021 | 142 | 145 | 47.395 | 40.430 |
| ALB | AL | AL | ALB | 8 | Albania | 2740 | 3153731 | 150 | 39 | 20.068 | 41.143 |
| ARM | AM | AM | ARM | 51 | Armenia | 2820 | 3017661 | 142 | 145 | 44.563 | 40.534 |
| AGO | AO | AO | AGO | 24 | Angola | 124670 | 16095214 | 2 | 17 | 17.544 | -12.296 |
length(wrld_simpl)## [1] 246
nrow(wrld_simpl@data)## [1] 246
ind <- which(wrld_simpl$ISO3=="DEU")plot(wrld_simpl[ind,])wrld_simpl@data[ind,]## FIPS ISO2 ISO3 UN NAME AREA POP2005 REGION SUBREGION LON LAT
## DEU GM DE DEU 276 Germany 34895 82652369 150 155 9.851 51.11
rasterraster nutzen kann.library(raster)
LUX1 <- getData('GADM', country='LUX', level=1)
plot(LUX1)kable(head(LUX1@data))| OBJECTID | ID_0 | ISO | NAME_0 | ID_1 | NAME_1 | HASC_1 | CCN_1 | CCA_1 | TYPE_1 | ENGTYPE_1 | NL_NAME_1 | VARNAME_1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 131 | LUX | Luxembourg | 1 | Diekirch | LU.DI | NA | District | District | Dikrech|Dikkrich | ||
| 2 | 131 | LUX | Luxembourg | 2 | Grevenmacher | LU.GR | NA | District | District | Gréivemaacher | ||
| 3 | 131 | LUX | Luxembourg | 3 | Luxembourg | LU.LU | NA | District | District | Lëtzebuerg|Luxemburg |
library(maptools)
krs <- readShapePoly("vg250_ebenen/vg250_krs.shp")
plot(krs)head(krs@data$RS)## [1] 03401 03458 09473 05962 10046 05916
## 402 Levels: 01001 01002 01003 01004 01051 01053 01054 01055 01056 ... 16077
BLA <- substr(krs@data$RS,1,2)
plot(krs[BLA=="08",])Quelle: Bundesnetzagentur
| VORWAHL | NAME | KENNUNG | |
|---|---|---|---|
| 0 | 04651 | Sylt | NA |
| 1 | 04668 | Klanxbüll | NA |
| 2 | 04664 | Neukirchen b Niebüll | NA |
| 3 | 04663 | Süderlügum | NA |
| 4 | 04666 | Ladelund | NA |
| 5 | 04631 | Glücksburg Ostsee | NA |
onb <- readShapePoly("onb_grenzen.shp")
kable(head(onb@data))vw_stg <- c("0711", "07121", "07122")
vw_reg_stg <- onb[onb@data$VORWAHL %in% vw_stg, ]
plot(vw_reg_stg)onbD
vwb <- as.character(onb@data$ONB_NUMMER)
vwb1 <- substr(vwb, 1,2)
vwb7 <- onb[vwb1=="07",]
plot(vwb7)rgdallibrary(rgdal)## OGR data source with driver: ESRI Shapefile
## Source: "post_pl.shp", layer: "post_pl"
## with 8270 features
## It has 3 fields
setwd("D:/GESIS/Workshops/GeoDaten/data/")
PLZ <- readOGR ("post_pl.shp","post_pl")library(rgdal)
PLZ <- readOGR ("post_pl.shp","post_pl")SG <- PLZ[PLZ@data$PLZORT99=="Stuttgart",]
plot(SG,col="chocolate1")BE <- PLZ[PLZ@data$PLZORT99%in%c("Berlin-West","Berlin (östl. Stadtbezirke)"),]
plot(BE,col="chocolate2")splibrary(sp)
spplot(wrld_simpl,"POP2005")colorRampslibrary(colorRamps)
spplot(wrld_simpl,"POP2005",col.regions=blue2red(100))colorRampsblue2green, blue2yellowspplot(wrld_simpl,"POP2005",col.regions=matlab.like(100))Sie können eine Statistik der Sparquote bei Eurostat downloaden.
http://ec.europa.eu/eurostat/web/euro-indicators/peeis
library(xlsx)
HHsr <- read.xlsx2("HHsavingRate.xls",1)url <- "https://raw.githubusercontent.com/Japhilko/
GeoData/master/2015/data/whcSites.csv"
whcSites <- read.csv(url) | name_en | date_inscribed | longitude | latitude | area_hectares | category | states_name_fr |
|---|---|---|---|---|---|---|
| Cultural Landscape and Archaeological Remains of the Bamiyan Valley | 2003 | 67.82525 | 34.84694 | 158.9265 | Cultural | Afghanistan |
| Minaret and Archaeological Remains of Jam | 2002 | 64.51606 | 34.39656 | 70.0000 | Cultural | Afghanistan |
| Historic Centres of Berat and Gjirokastra | 2005 | 20.13333 | 40.06944 | 58.9000 | Cultural | Albanie |
| Butrint | 1992 | 20.02611 | 39.75111 | NA | Cultural | Albanie |
| Al Qal’a of Beni Hammad | 1980 | 4.78684 | 35.81844 | 150.0000 | Cultural | Algérie |
| M’Zab Valley | 1982 | 3.68333 | 32.48333 | 665.0300 | Cultural | Algérie |
OpenStreetMap.org ist ein im Jahre 2004 gegründetes internationales Projekt mit dem Ziel, eine freie Weltkarte zu erschaffen. Dafür sammeln wir weltweit Daten über Straßen, Eisenbahnen, Flüsse, Wälder, Häuser und vieles mehr.
<www.openstreetmap.org/export>
(load("data/info_bar_Berlin.RData"))## [1] "info"
| addr.postcode | addr.street | name | lat | lon | |
|---|---|---|---|---|---|
| 79675952 | 13405 | Scharnweberstraße | Albert’s | 52.56382 | 13.32885 |
| 86005430 | NA | NA | Newton Bar | 52.51293 | 13.39123 |
| 111644760 | NA | NA | No Limit Shishabar | 52.56556 | 13.32093 |
| 149607257 | NA | NA | en passant | 52.54420 | 13.41298 |
| 248651127 | 10115 | Bergstraße | Z-Bar | 52.52953 | 13.39564 |
| 267780050 | 10405 | Christburger Straße | Immertreu | 52.53637 | 13.42509 |
tab_plz <- table(info_be$addr.postcode)ind <- match(BE@data$PLZ99_N,names(tab_plz))
ind## [1] 1 2 3 4 5 6 7 8 NA 9 NA NA NA NA NA 10 11 12 NA 13 14 15 16
## [24] 17 18 19 20 21 22 23 24 25 NA 26 27 28 29 NA NA NA NA 30 NA 31 32 33
## [47] 34 35 NA NA 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 NA 52 53
## [70] NA 54 55 NA NA NA 56 57 58 59 60 NA NA NA NA NA 61 NA NA NA 62 NA NA
## [93] NA NA NA NA NA NA NA 63 NA NA 64 NA 65 NA NA NA 66 NA NA NA NA 67 NA
## [116] NA 68 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [139] NA 69 70 NA 71 72 73 74 75 NA 76 NA NA NA NA NA NA NA NA NA NA NA NA
## [162] 77 NA 78 79 NA NA NA NA 80 NA NA NA NA 81 NA 82 83 84 NA NA NA NA NA
## [185] NA NA NA 85 NA NA
BE@data$num_plz <- tab_plz[ind]tmaplibrary(tmap)BE@data$num_plz[is.na(BE@data$num_plz)] <- 0
qtm(BE,fill = "num_plz")load("data/osmsa_PLZ_14.RData")| PLZ99 | PLZ99_N | PLZORT99 | nname | EWZ_gem | area_d | EWZ_gemplz | place_id | osm_type | osm_id | lat | lon | display_name | class | type | importance | state | city | county | plz2ort | bakery | bar | biergarten | butcher | cafe | chemist | clothes | college | store | food | general | cream | kiosk | mall | pub | restaurant | supermarket | population_density | BLA | gadmbla | gadmkreis | stop | yes | gadmgem | gadmgemtyp | gadmgem2 | gadmgemtyp2 | ort2plz | ODdat | zenEinw | crossing | bus_stop | street_lamp | traffic_signals | land_cover.index | land_cover.value | land_cover.description | elevation.value | temp_Jan | temp_Feb | temp_Mar | temp_Apr | temp_May | temp_Jun | temp_Jul | temp_Aug | temp_Sep | temp_Oct | temp_Nov | temp_Dez | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01067 | 1067 | Dresden | Dresden, Stadt | 512354 | 0.0008602 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 17 | 10 | 0 | 4 | 28 | 2 | 45 | 0 | 0 | 21 | 0 | 1 | 3 | 0 | 8 | 100 | 6 | 567 | 14 | Sachsen | Dresden | 101 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 4.0 | 121 | 48 | 162 | 87 | 22 | Artificial surfaces and associated areas | urban, water, vegetation, mountains, etc. | 112 | -0.7 | 0.4 | 3.9 | 8.4 | 13.3 | 16.9 | 18.5 | 18.0 | 14.3 | 9.8 | 4.4 | 1.0 |
| 1 | 01069 | 1069 | Dresden | Dresden, Stadt | 512354 | 0.0006819 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 20 | 6 | 0 | 9 | 24 | 5 | 41 | 0 | 0 | 28 | 0 | 0 | 3 | 0 | 2 | 22 | 9 | 498 | 14 | Sachsen | Dresden | 83 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 5.0 | 113 | 40 | 105 | 96 | 22 | Artificial surfaces and associated areas | urban, water, vegetation, mountains, etc. | 115 | -0.8 | 0.3 | 3.8 | 8.4 | 13.3 | 16.7 | 18.4 | 17.9 | 14.4 | 9.9 | 4.4 | 1.0 |
| 2 | 01097 | 1097 | Dresden | Dresden, Stadt | 512354 | 0.0004382 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 22 | 9 | 0 | 4 | 22 | 3 | 28 | 0 | 0 | 23 | 0 | 0 | 3 | 0 | 15 | 49 | 14 | 567 | 14 | Sachsen | Dresden | 40 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 5.5 | 98 | 20 | 33 | 55 | 22 | Artificial surfaces and associated areas | urban, water, vegetation, mountains, etc. | 115 | -0.7 | 0.3 | 3.8 | 8.4 | 13.3 | 16.7 | 18.4 | 18.0 | 14.4 | 9.9 | 4.5 | 1.0 |
| 3 | 01099 | 1099 | Dresden | Dresden, Stadt | 512354 | 0.0067740 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 18 | 35 | 5 | 2 | 35 | 1 | 33 | 0 | 1 | 30 | 0 | 2 | 0 | 0 | 25 | 59 | 6 | 567 | 14 | Sachsen | Dresden | 88 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 0.0 | 38 | 41 | 24 | 37 | 4 | Tree Cover, needle-leaved, evergreen | urban, water, vegetation, mountains, etc. | 250 | -1.2 | -0.3 | 3.1 | 7.6 | 12.5 | 16.0 | 17.6 | 17.4 | 13.7 | 9.3 | 3.8 | 0.4 |
| 4 | 01109 | 1109 | Dresden | Dresden, Stadt | 512354 | 0.0034973 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 14 | 0 | 0 | 3 | 4 | 1 | 5 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 17 | 4 | 567 | 14 | Sachsen | Dresden | 242 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 1.5 | 47 | 119 | 230 | 58 | 22 | Artificial surfaces and associated areas | urban, water, vegetation, mountains, etc. | 216 | -1.0 | -0.1 | 3.2 | 7.8 | 12.7 | 16.1 | 17.7 | 17.6 | 13.9 | 9.3 | 3.9 | 0.6 |
| 5 | 01127 | 1127 | Dresden | Dresden, Stadt | 512354 | 0.0003626 | 20494.16 | 144969068 | relation | 191645 | 51.0493286 | 13.7381437 | Dresden, Sachsen, Deutschland | place | city | 0.8162766 | Sachsen | Dresden | Dresden | 25 | 6 | 1 | 0 | 3 | 4 | 0 | 6 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 5 | 13 | 3 | 567 | 14 | Sachsen | Dresden | 44 | 0 | Dresden | Einheitsgemeinde | Dresden | Stadt | 1 | 0 | 4.5 | 204 | 22 | 36 | 12 | 22 | Artificial surfaces and associated areas | urban, water, vegetation, mountains, etc. | 112 | -0.7 | 0.4 | 3.9 | 8.4 | 13.4 | 16.9 | 18.4 | 18.0 | 14.4 | 9.8 | 4.4 | 1.1 |
qtm(PLZ_SG,fill="bakery")kable(PLZ_SG@data[which.max(PLZ_SG$bakery),c("PLZ99","lat","lon","bakery")])| PLZ99 | lat | lon | bakery | |
|---|---|---|---|---|
| 4964 | 70173 | 48.7784485 | 9.1800132 | 30 |
ggmaplibrary(ggmap)
lon_plz <- PLZ_SG@data[which.max(PLZ_SG$bakery),"lon"]
lat_plz <- PLZ_SG@data[which.max(PLZ_SG$bakery),"lat"]
mp_plz <- as.numeric(c(lon_plz,lat_plz))
qmap(location = mp_plz,zoom=15)PLZ_SG <- PLZ[PLZ@data$PLZORT99=="Stuttgart",]| Type_landcover | Freq |
|---|---|
| Artificial surfaces and associated areas | 26 |
| Cultivated and managed areas | 8 |
| Tree Cover, needle-leaved, evergreen | 1 |
qtm(PLZ_SG,fill="land_cover.value")qtm(PLZ_SG,fill="elevation.value")osmarlibrary(osmar) src <- osmsource_api()
gc <- geocode("Stuttgart-Degerloch")
bb <- center_bbox(gc$lon, gc$lat, 800, 800)
ua <- get_osm(bb, source = src)
plot(ua)